Jure Pražnikar

Community detection is essential for uncovering the functional organization of complex networks. While traditional methods often rely on edge density, motif-based approaches use higher-order structural patterns to identify communities. However, existing research frequently employs conventional motifs, such as triangles or 4-node cliques, or lacks validation against networks with ground-truth communities. This study addresses these limitations by systematically evaluating eight small motifs across both synthetic and real-world networks with known community structures. We propose a framework that transforms unweighted graphs into weighted representations by assigning weights to node pairs based on their co-occurrence frequency within specific graphlets, while also preserving information about the original edges, rather than creating a potentially sparse (hyper)network. Thus, graphlet adjacency captures the topological complexity of a node by accounting for both its direct edges and the local connectivity patterns of its neighbors; this higher-order information is vital for accurate community detection. Our results demonstrate that graphlet-based weighting significantly enhances community detection in networks. We find that no single "universal" motif optimizes performance across all real-world networks. Rather than favoring only dense, clique-based structures, our findings highlight that simpler motifs can also provide strong performance in networks. These results suggest that relying exclusively on cliques may overlook critical connectivity patterns, offering a new perspective on how higher-order structures define communities in networks.

Motivation: Advances in hardware have made molecular dynamics (MD) simulations of protein structures faster and more accessible to the scientific community. However, accurately estimating protein flexibility using MD remains computationally demanding, especially for large systems and long time scales. Several MD-based resources—including MdMD, the DynamD database, and more recently ATLAS and mdCATH—now provide MD trajectories for thousands of proteins, enabling the development of predictive models. Results: Here, the Graphlet Degree Vector (GDV) is introduced as a lightweight, fast, and easy-to-implement linear model for predicting protein flexibility directly from atom coordinates. GDV is a 15-dimensional feature vector that captures local packing and the spatial connectivity of each atom with its nearby neighbors. Trained on a subset of globular-like proteins from the ATLAS database, the GDV model achieves a Spearman correlation of 0.828 compared to MD data. The model trained on ATLAS dataset was further evaluated on independent Nuclear Magnetic Resonance and cryo-electron microscopy datasets, demonstrating the robustness and generalizability of the GDV-based approach. A key advantage of the GDV model is that it requires no additional external or experimental data and can be applied in near real time (on the order of 10 seconds) even for large proteins with 20 000 atoms on a standard desktop or laptop. Overall, the results show that a lightweight, fast, and purely coordinate-based model can provide accurate and generalizable predictions of protein flexibility across diverse folds and sizes. Availability: The source code is available in the GitHub repository https://github.com/jure-praznikar/FastProtFlex. The data required for model training are available at https://doi.org/10.5281/zenodo.17771418. Contact: jure.praznikar@upr.si

Advances in hardware have made molecular dynamics (MD) simulations of protein structures faster and more accessible to the scientific community. However, accurately estimating protein flexibility using MD remains computationally demanding, especially for large systems and long time scales. Several MD-based resources – including MdMD, the DynamD database, and more recently ATLAS and mdCATH – now provide MD trajectories for thousands of proteins, enabling the development of predictive models. Here, the Graphlet Degree Vector (GDV) is introduced as a lightweight, fast, and easy-to-implement linear model for predicting protein flexibility directly from atom coordinates. GDV is a 15-dimensional feature vector that captures local packing and the spatial connectivity of each atom with its nearby neighbors. Trained on a subset of globular-like proteins from the ATLAS database, the GDV model achieves a Spearman correlation of 0.828 compared to MD data. The model trained on ATLAS dataset was further evaluated on independent Nuclear Magnetic Resonance and cryo-electron microscopy datasets, demonstrating the robustness and generalizability of the GDV-based approach. A key advantage of the GDV model is that it requires no additional external or experimental data and can be applied in near real time (on the order of 10 seconds) even for large proteins with 20,000 atoms on a standard desktop or laptop. Overall, the results show that a lightweight, fast, and purely coordinate-based model can provide accurate and generalizable predictions of protein flexibility across diverse folds and sizes. The source code is available in the GitHub repository https://github.com/jure-praznikar/FastProtFlex . The data required for model training are available at https://doi.org/10.5281/zenodo.17771418 .

Understanding protein-ligand binding site behavior is central to structure-based drug design. We analyzed amino acid composition and interactions in protein-ligand small-molecule binding sites and developed a novel method for binding site prediction. We analyzed the PDBBind+ database, which contains the largest protein-ligand binding site dataset known to us, using existing cheminformatics packages and in-house code. We used the resulting data to train a binding site prediction model. Within solvent-accessible binding regions, tryptophan, phenylalanine, tyrosine, methionine, and glycine, were enriched. Interaction analysis revealed hydrophobic contacts as the most frequent, followed by hydrogen bonds, water-bridged hydrogen bonds, salt bridges, π-π, π-cation, and occasional halogen interactions. We introduced the amino acid binding site enrichment index (ABSE), to support small-molecule binding site detection, and developed a model that discriminates binding site sequences from protein surface patches with 0.91 accuracy. This work offers interpretable composition-interaction relationships and practical tool for binding site characterization. To facilitate application, we provide a free, open-source, fast, bindingsite identification tool (AABS), available at https://gitlab.com/Jukic/aabs. We anticipate that these findings and tool will advance binding site prediction and accelerate computationally intensive drug discovery within medicinal chemistry.

Background: Early time-restricted eating combined with energy restriction (eTRE + ER) has been shown to reduce fat mass, diastolic blood pressure (DBP) and fasting glucose more effectively than late TRE with energy restriction (lTRE + ER) or energy restriction (ER) alone. Given the gut microbiome’s sensitivity to circadian rhythms, we examined whether adding TRE, particularly eTRE, to ER alters gut microbiota composition beyond ER alone, and whether such effects persist during follow-up. Methods: We analysed anthropometric, biochemical and gut microbiome data from 76 participants at baseline and after a 3-month intervention (eTRE + ER: n = 33; lTRE + ER: n = 23; ER: n = 20). Follow-up microbiome data 6-months after the end of intervention were available for 43 participants. Gut microbiota composition was assessed via 16S rRNA gene sequencing of stool samples. Results: No significant between-group differences in beta diversity were observed over time. However, changes in alpha diversity differed significantly across groups at the end of the intervention (Shannon: F = 5.72, p < 0.001; Simpson: F = 6.72, p < 0.001; Richness: F = 3.99, p = 0.01) and at follow-up (Richness: F = 3.77, p = 0.02). lTRE + ER led to the greatest reductions in diversity post intervention, while ER was least favourable during follow-up. Although no significant between-group differences were observed at the phylum level either at the end of the intervention or during follow-up, only the eTRE + ER group exhibited a significant decrease in Bacillota and an increase in Bacteroidota during follow-up. At the genus level, differential abundance analysis revealed significant shifts in taxa such as Faecalibacterium, Subdoligranulum, and other genera within the Ruminococcaceae and Oscillospiraceae families. In the eTRE + ER, Faecalibacterium and Subdoligranulum increased, while in other groups decreased. Notably, the changes in Faecalibacterium were negatively correlated with fasting glucose, while the increase in Subdoligranulum was inversely associated with DBP; however, both associations were weak in strength. Conclusions: eTRE + ER may promote beneficial, lasting shifts in the gut microbiome associated with improved metabolic outcomes. These results support further research into personalized TRE strategies for treatment of obesity.

The B-factor or temperature factor is one of the most important parameters in addition to the atomic coordinates, and which is refined during the determination of the protein structure and stored in the Protein Data Bank. It reflects the uncertainty of the atomic positions and is closely linked to atomic flexibility. By using graphlet degree vectors as feature descriptors in a linear model—together with appropriate data transformation and consideration of various experimental factors—the model provides better prediction results. For example, the inclusion of crystal contacts in the linear model significantly improves the prediction accuracy. Since the distributions of the B-factors typically follow an inverse gamma distribution, applying a logarithmic transformation further improves the performance of the model. It has also been shown that large ligands, such as those found in protein–DNA complexes, have a significant impact on the quality of the prediction. A linear model based on graphlet degree vectors proves to be effective not only for the prediction of B-factors and the validation of deposited protein structures but also for the qualitative estimation of root-mean-square fluctuations derived from molecular dynamics.

Present and future Köppen-Geiger climate maps and time series of particulate matter (PM10) at continental scale were used to investigate the relationship between spatial patterns of PM10 and climate zones. Five main patterns (spatial clusters) of PM10 were found over the European continent, which show a good spatial overlap with the main Köppen-Geiger climate zones. The map of future climate, which shows a poleward movement of the warmer climate zones, an expansion of the (semi-)arid zones and a shrinking of the polar and tundra zones, indicates a higher PM10 concentration, especially in the northern part of Europe. The results also show that there is a temporal shift (later/earlier) of PM10 extremes in the period 2013–2022. This insight into the relationship between climate zones and clustered PM10 time series and the use of high-quality future climate maps based on different scenarios can be used to estimate possible changes, such as annual averages or seasonal cycles, in PM10 concentrations.

In structural biology, atomic displacement parameters, commonly used in the form of B values, describe uncertainties in atomic positions. Their distribution over the structure can provide hints on local structural reliability and mobility. A spatial macromolecular model can be represented by a graph whose nodes are atoms and whose edges correspond to all interatomic contacts within a certain distance. Small connected subgraphs, called graphlets, provide information about the wiring of a particular atom. The multiple linear regression approach based on this information aims to predict a distribution of values of isotropic atomic displacement parameters (B values) within a protein structure, given the atomic coordinates and molecular packing. By modeling the dynamic component of atomic uncertainties, this method allows the B values obtained from experimental crystallographic or cryo-electron microscopy studies to be reproduced relatively well.

Despite the known effects of diet on gut microbiota composition, not many studies have evaluated the relationship between distinct dietary patterns and gut microbiota. The aim of our study was to determine whether gut microbiota composition could be a useful indicator of a long-term dietary pattern. We collected data from 89 subjects adhering to omnivorous, vegetarian, vegan, and low-carbohydrate, high-fat diet that were equally distributed between groups and homogenous by age, gender, and BMI. Gut microbiota composition was analyzed with a metabarcoding approach using V4 hypervariable region of the 16S rRNA gene. K-means clustering of gut microbiota at the genus level was performed and the nearest neighbor classifier was applied to predict microbiota clustering classes. Our results suggest that gut microbiota composition at the genus level is not a useful indicator of a subject’s dietary pattern, with the exception of a vegan diet that is represented by a high abundance of Prevotella 9. Based on our model, a combination of 26 variables (anthropometric measurements, serum biomarkers, lifestyle factors, gastrointestinal symptoms, psychological factors, specific nutrients intake) is more important to predict an individual’s microbiota composition cluster, with 91% accuracy, than the dietary intake alone. Our findings could serve to develop strategies to educate individuals about changes of some modifiable lifestyle factors, aiming to classify them into clusters with favorable health markers, independent of their dietary pattern.